R Notebook
Principles of Data Visualization and Introduction to ggplot2
I have provided you with data about the 5,000 fastest growing companies in the US, as compiled by Inc. magazine. lets read this in:
inc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE)And lets preview this data:
head(inc)## Rank Name Growth_Rate Revenue
## 1 1 Fuhu 421.48 1.179e+08
## 2 2 FederalConference.com 248.31 4.960e+07
## 3 3 The HCI Group 245.45 2.550e+07
## 4 4 Bridger 233.08 1.900e+09
## 5 5 DataXu 213.37 8.700e+07
## 6 6 MileStone Community Builders 179.38 4.570e+07
## Industry Employees City State
## 1 Consumer Products & Services 104 El Segundo CA
## 2 Government Services 51 Dumfries VA
## 3 Health 132 Jacksonville FL
## 4 Energy 50 Addison TX
## 5 Advertising & Marketing 220 Boston MA
## 6 Real Estate 63 Austin TXsummary(inc)## Rank Name Growth_Rate
## Min. : 1 (Add)ventures : 1 Min. : 0.340
## 1st Qu.:1252 @Properties : 1 1st Qu.: 0.770
## Median :2502 1-Stop Translation USA: 1 Median : 1.420
## Mean :2502 110 Consulting : 1 Mean : 4.612
## 3rd Qu.:3751 11thStreetCoffee.com : 1 3rd Qu.: 3.290
## Max. :5000 123 Exteriors : 1 Max. :421.480
## (Other) :4995
## Revenue Industry Employees
## Min. :2.000e+06 IT Services : 733 Min. : 1.0
## 1st Qu.:5.100e+06 Business Products & Services: 482 1st Qu.: 25.0
## Median :1.090e+07 Advertising & Marketing : 471 Median : 53.0
## Mean :4.822e+07 Health : 355 Mean : 232.7
## 3rd Qu.:2.860e+07 Software : 342 3rd Qu.: 132.0
## Max. :1.010e+10 Financial Services : 260 Max. :66803.0
## (Other) :2358 NA's :12
## City State
## New York : 160 CA : 701
## Chicago : 90 TX : 387
## Austin : 88 NY : 311
## Houston : 76 VA : 283
## San Francisco: 75 FL : 282
## Atlanta : 74 IL : 273
## (Other) :4438 (Other):2764Think a bit on what these summaries mean. Use the space below to add some more relevant non-visual exploratory information you think helps you understand this data:
# Insert your code here, create more chunks as necessary
if(!(c("psych") %in% rownames(installed.packages()))) {install.packages('psych')}
library(psych)
if(!(c("pastecs") %in% rownames(installed.packages()))) {install.packages('pastecs')}
library(pastecs)## Loading required package: boot##
## Attaching package: 'boot'## The following object is masked from 'package:psych':
##
## logitstat.desc(inc) ## Rank Name Growth_Rate Revenue Industry
## nbr.val 5.001000e+03 NA 5.001000e+03 5.001000e+03 NA
## nbr.null 0.000000e+00 NA 0.000000e+00 0.000000e+00 NA
## nbr.na 0.000000e+00 NA 0.000000e+00 0.000000e+00 NA
## min 1.000000e+00 NA 3.400000e-01 2.000000e+06 NA
## max 5.000000e+03 NA 4.214800e+02 1.010000e+10 NA
## range 4.999000e+03 NA 4.211400e+02 1.009800e+10 NA
## sum 1.251071e+07 NA 2.306374e+04 2.411609e+11 NA
## median 2.502000e+03 NA 1.420000e+00 1.090000e+07 NA
## mean 2.501641e+03 NA 4.611826e+00 4.822254e+07 NA
## SE.mean 2.041222e+01 NA 1.997192e-01 3.401441e+06 NA
## CI.mean.0.95 4.001690e+01 NA 3.915372e-01 6.668317e+06 NA
## var 2.083710e+06 NA 1.994787e+02 5.786059e+16 NA
## std.dev 1.443506e+03 NA 1.412369e+01 2.405423e+08 NA
## coef.var 5.770237e-01 NA 3.062495e+00 4.988172e+00 NA
## Employees City State
## nbr.val 4.989000e+03 NA NA
## nbr.null 0.000000e+00 NA NA
## nbr.na 1.200000e+01 NA NA
## min 1.000000e+00 NA NA
## max 6.680300e+04 NA NA
## range 6.680200e+04 NA NA
## sum 1.161030e+06 NA NA
## median 5.300000e+01 NA NA
## mean 2.327180e+02 NA NA
## SE.mean 1.915720e+01 NA NA
## CI.mean.0.95 3.755654e+01 NA NA
## var 1.830955e+06 NA NA
## std.dev 1.353128e+03 NA NA
## coef.var 5.814454e+00 NA NAdescribe(inc)## vars n mean sd median trimmed
## Rank 1 5001 2501.64 1443.51 2.502e+03 2501.73
## Name* 2 5001 2501.00 1443.81 2.501e+03 2501.00
## Growth_Rate 3 5001 4.61 14.12 1.420e+00 2.14
## Revenue 4 5001 48222535.49 240542281.14 1.090e+07 17334966.26
## Industry* 5 5001 12.10 7.33 1.300e+01 12.05
## Employees 6 4989 232.72 1353.13 5.300e+01 81.78
## City* 7 5001 732.00 441.12 7.610e+02 731.74
## State* 8 5001 24.80 15.64 2.300e+01 24.44
## mad min max range skew kurtosis
## Rank 1853.25 1.0e+00 5.0000e+03 4.9990e+03 0.00 -1.20
## Name* 1853.25 1.0e+00 5.0010e+03 5.0000e+03 0.00 -1.20
## Growth_Rate 1.22 3.4e-01 4.2148e+02 4.2114e+02 12.55 242.34
## Revenue 10674720.00 2.0e+06 1.0100e+10 1.0098e+10 22.17 722.66
## Industry* 8.90 1.0e+00 2.5000e+01 2.4000e+01 -0.10 -1.18
## Employees 53.37 1.0e+00 6.6803e+04 6.6802e+04 29.81 1268.67
## City* 604.90 1.0e+00 1.5190e+03 1.5180e+03 -0.04 -1.26
## State* 19.27 1.0e+00 5.2000e+01 5.1000e+01 0.12 -1.46
## se
## Rank 20.41
## Name* 20.42
## Growth_Rate 0.20
## Revenue 3401441.44
## Industry* 0.10
## Employees 19.16
## City* 6.24
## State* 0.22tail(inc)## Rank Name Growth_Rate Revenue
## 4996 4996 cSubs 0.34 1.34e+07
## 4997 4997 Dot Foods 0.34 4.50e+09
## 4998 4998 Lethal Performance 0.34 6.80e+06
## 4999 4999 ArcaTech Systems 0.34 3.26e+07
## 5000 5000 INE 0.34 6.80e+06
## 5001 5000 ALL4 0.34 4.70e+06
## Industry Employees City State
## 4996 Business Products & Services 19 Montvale NJ
## 4997 Food & Beverage 3919 Mt. Sterling IL
## 4998 Retail 8 Wellington FL
## 4999 Financial Services 63 Mebane NC
## 5000 IT Services 35 Bellevue WA
## 5001 Environmental Services 34 Kimberton PAtable(inc$Industry)##
## Advertising & Marketing Business Products & Services
## 471 482
## Computer Hardware Construction
## 44 187
## Consumer Products & Services Education
## 203 83
## Energy Engineering
## 109 74
## Environmental Services Financial Services
## 51 260
## Food & Beverage Government Services
## 131 202
## Health Human Resources
## 355 196
## Insurance IT Services
## 50 733
## Logistics & Transportation Manufacturing
## 155 256
## Media Real Estate
## 54 96
## Retail Security
## 203 73
## Software Telecommunications
## 342 129
## Travel & Hospitality
## 62Question 1
Create a graph that shows the distribution of companies in the dataset by State (ie how many are in each state). There are a lot of States, so consider which axis you should use. This visualization is ultimately going to be consumed on a ‘portrait’ oriented screen (ie taller than wide), which should further guide your layout choices.
# Answer Question 1 here
if(!(c("ggplot2") %in% rownames(installed.packages()))) {install.packages('ggplot2')}
library(ggplot2)##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphaggp<-ggplot(data=inc, aes(x=State))
# counts
ggp + geom_histogram(fill="black",stat = "count") + coord_flip()+
theme_classic()+
labs(title="Distribution of Companies", x="State",y="Number of Companies")## Warning: Ignoring unknown parameters: binwidth, bins, pad
Quesiton 2
Lets dig in on the state with the 3rd most companies in the data set. Imagine you work for the state and are interested in how many people are employed by companies in different industries. Create a plot that shows the average and/or median employment by industry for companies in this state (only use cases with full data, use R’s complete.cases() function.) In addition to this, your graph should show how variable the ranges are, and you should deal with outliers.
Let’s rank the states by the number of companies they have, and find the 3rd state with most employees
if(!(c("dplyr") %in% rownames(installed.packages()))) {install.packages('dplyr')}
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:pastecs':
##
## first, last## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondf <- group_by(inc, State)
df <-summarise(df,count = n())
df <- arrange(df, desc(count))
#state with 3rd most companies is NY
df$State[3]## [1] NY
## 52 Levels: AK AL AR AZ CA CO CT DC DE FL GA HI IA ID IL IN KS KY LA ... WYWe can see that the state with 3rd most companies is NY
Let’s filter the data for only NY state records and return only the records with no missing values
NY_inc <- filter(inc, State == df$State[3])
summary(NY_inc)## Rank Name Growth_Rate
## Min. : 26 1st Equity : 1 Min. : 0.350
## 1st Qu.:1186 33Across : 1 1st Qu.: 0.670
## Median :2702 5Linx Enterprises : 1 Median : 1.310
## Mean :2612 Access Display Group: 1 Mean : 4.371
## 3rd Qu.:4005 Adafruit : 1 3rd Qu.: 3.580
## Max. :4981 AdCorp Media Group : 1 Max. :84.430
## (Other) :305
## Revenue Industry Employees
## Min. :2.000e+06 Advertising & Marketing : 57 Min. : 1.0
## 1st Qu.:4.300e+06 IT Services : 43 1st Qu.: 21.0
## Median :8.800e+06 Business Products & Services: 26 Median : 45.0
## Mean :5.872e+07 Consumer Products & Services: 17 Mean : 271.3
## 3rd Qu.:2.570e+07 Telecommunications : 17 3rd Qu.: 105.5
## Max. :4.600e+09 Education : 14 Max. :32000.0
## (Other) :137
## City State
## New York :160 NY :311
## Brooklyn : 15 AK : 0
## Rochester: 9 AL : 0
## Buffalo : 5 AR : 0
## Fairport : 5 AZ : 0
## new york : 5 CA : 0
## (Other) :112 (Other): 0NY_inc <- NY_inc[complete.cases(NY_inc),]Let’s summarize the data and identify outliers outliers are those observations that lie outside 1.5*IQR, where IQR, the ‘Inter Quartile Range’ is the difference between 75th and 25th quartiles
# aggregate employees by Industry
Employment_by_Industry<- group_by(NY_inc,Industry) %>%
summarise(Avg_employee_cnt = mean(Employees), Median_Employee_cnt = median(Employees), Total_employee_cnt = sum(Employees)
, Min_employee_cnt = min (Employees), Max_employee_cnt = max(Employees) ,
high_Outlier_limit = quantile (Employees)[4] + 1.5*IQR(Employees), low_Outlier_limit = quantile (Employees)[2] - 1.5*IQR(Employees)
,First_quartile = quantile(Employees)[2],
First_quartile = quantile(Employees)[2] , third_quartile = quantile (Employees)[4])
Employment_by_Industry## # A tibble: 25 x 10
## Industry Avg_employee_cnt Median_Employee_c~ Total_employee_~
## <fct> <dbl> <dbl> <int>
## 1 Advertising & Mar~ 58.4 38.0 3331
## 2 Business Products~ 1492 70.5 38804
## 3 Computer Hardware 44.0 44.0 44
## 4 Construction 61.0 24.5 366
## 5 Consumer Products~ 626 25.0 10647
## 6 Education 59.9 50.5 838
## 7 Energy 129 120 646
## 8 Engineering 53.5 54.5 214
## 9 Environmental Ser~ 155 155 310
## 10 Financial Services 144 81.0 1876
## # ... with 15 more rows, and 6 more variables: Min_employee_cnt <dbl>,
## # Max_employee_cnt <dbl>, high_Outlier_limit <dbl>,
## # low_Outlier_limit <dbl>, First_quartile <dbl>, third_quartile <dbl>Let’s create a plot that shows the average and median employment by industry for companies in NY state, without dealing with outliers and showing variability using boxplots. Points outside the whiskers of the boxplot are outliers
g <- ggplot(NY_inc, aes(x = Industry, y = Employees)) +
geom_boxplot()
g+ coord_flip() 
Let’s remove the outliers and reproduce the boxplot
g <- ggplot(NY_inc, aes(x = Industry, y = Employees)) +
geom_boxplot(outlier.shape = NA)
g + coord_flip() +
scale_y_continuous(limits = quantile(NY_inc$Employees, c(0.1, 0.9)))## Warning: Removed 62 rows containing non-finite values (stat_boxplot).
Question 3
Now imagine you work for an investor and want to see which industries generate the most revenue per employee. Create a chart that makes this information clear. Once again, the distribution per industry should be shown.
Let’s get only the cases without missing values
# Answer Question 3 here
INC_2 <- inc[complete.cases(inc),]
INC_summary <- group_by(INC_2,Industry) %>%
summarise( Total_employee_cnt = sum(Employees)
, Total_Revenue = sum (Revenue), Revenue_per_employee = sum (Revenue)/sum(Employees))
INC_summary2 <-arrange(INC_summary, desc(Revenue_per_employee))
INC_summary2## # A tibble: 25 x 4
## Industry Total_employee_c~ Total_Revenue Revenue_per_emplo~
## <fct> <int> <dbl> <dbl>
## 1 Computer Hardware 9714 11885700000 1223564
## 2 Energy 26437 13771600000 520921
## 3 Construction 29099 13174300000 452741
## 4 Logistics & Transpo~ 39994 14837800000 371001
## 5 Consumer Products &~ 45464 14956400000 328972
## 6 Insurance 7339 2337900000 318558
## 7 Manufacturing 43942 12603600000 286824
## 8 Retail 37068 10257400000 276718
## 9 Financial Services 47693 13150900000 275741
## 10 Environmental Servi~ 10155 2638800000 259852
## # ... with 15 more rowsggp<- ggplot(data=INC_summary2, aes(x=Industry, y=Revenue_per_employee))
# counts
ggp + geom_bar(fill="black",stat = "identity") + coord_flip()